Help: How to get components values?
Closed this issue · 4 comments
leoplct commented
Hello,
I am looking for a way to get the components of the forecasted series.
I know I could use this but I need the numeric values, not the chart.
m.plot_components(forecast).savefig("components.png")
Inspecting the code I found this
model.predict(future_days(days: days))
puts model.seasonalities.inspect
but the output is this
{"weekly"=>{:period=>7, :fourier_order=>3, :prior_scale=>10.0, :mode=>"multiplicative", :condition_name=>nil}}
{"weekly"=>{:period=>7, :fourier_order=>3, :prior_scale=>10.0, :mode=>"multiplicative", :condition_name=>nil}}
leoplct commented
@ankane The method .seasonalities doesn't exist.
r = model.predict(future_days(days: days))
puts r.seasonalities.inspect
/app/services/forecaster/forecaster.rb:8:in `results': undefined method `seasonalities' for ds trend yhat_lower yhat_upper trend_lower (NoMethodError)
2020-01-01 00:00:00 UTC 132.457668 102.54412310794025 160.7679458245698 132.457668
2020-01-02 00:00:00 UTC 135.52352290909093 106.29359243905199 163.72024324319386 135.52352290909093
2020-01-03 00:00:00 UTC 138.58937782025072 108.6678132212479 169.89344531863384 138.58937782025072
2020-01-04 00:00:00 UTC 141.65523271340436 110.02777864484085 171.52578446670833 141.65523271340436
2020-01-05 00:00:00 UTC 144.7210876055361 114.07941973515894 173.76046504415518 144.7210876055361
... ... ... ... ...
2020-02-07 00:00:00 UTC 245.89429893658695 215.64588805741883 277.5255988074532 245.89427912161068
2020-02-08 00:00:00 UTC 248.96015382504646 219.92458120979668 279.48258544843526 248.96013285568122
2020-02-09 00:00:00 UTC 252.0260087135059 225.84879229851205 281.4402568223808 252.02598634948285
2020-02-10 00:00:00 UTC 255.09186360196537 226.73538315722791 287.3103036278492 255.0918399893096
2020-02-11 00:00:00 UTC 258.1577184904248 228.4267107916969 285.5872656861613 258.15769368316364
trend_upper Amazon events Amazon events_lower Amazon events_upper holidays holidays_lower holidays_upper
132.457668 0.0 0.0 0.0 0.0 0.0 0.0
135.52352290909093 0.0 0.0 0.0 0.0 0.0 0.0
138.58937782025072 0.0 0.0 0.0 0.0 0.0 0.0
141.65523271340436 0.0 0.0 0.0 0.0 0.0 0.0
144.7210876055361 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ...
245.89431759518342 0.0 0.0 0.0 0.0 0.0 0.0
248.96017402538914 0.0 0.0 0.0 0.0 0.0 0.0
252.02602978164828 0.0 0.0 0.0 0.0 0.0 0.0
255.09188646235285 0.0 0.0 0.0 0.0 0.0 0.0
258.1577422432756 0.0 0.0 0.0 0.0 0.0 0.0
multiplicative_terms multiplicative_terms_lower multiplicative_terms_upper col col_lower col_upper additive_terms
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ...
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
additive_terms_lower additive_terms_upper yhat
0.0 0.0 132.457668
0.0 0.0 135.52352290909093
0.0 0.0 138.58937782025072
0.0 0.0 141.65523271340436
0.0 0.0 144.7210876055361
... ... ...
0.0 0.0 245.89429893658695
0.0 0.0 248.96015382504646
0.0 0.0 252.0260087135059
0.0 0.0 255.09186360196537
0.0 0.0 258.1577184904248:Rover::DataFrame
puts r.seasonalities.inspect
^^^^^^^^^^^^^^
ankane commented
If there's weekly seasonality, there will be weekly, weekly_lower, and weekly_upper columns in the data frame.